Facial Sentiment Analysis Using AI Techniques: State-of-the-Art, Taxonomies, and Challenges

Keyur Patel, Dev Mehta, Chinmay Mistry, Rajesh Gupta, Sudeep Tanwar, Neeraj Kumar, Mamoun Alazab

    Research output: Contribution to journalArticlepeer-review

    95 Citations (Scopus)
    131 Downloads (Pure)


    With the advancements in machine and deep learning algorithms, the envision of various critical real-life applications in computer vision becomes possible. One of the applications is facial sentiment analysis. Deep learning has made facial expression recognition the most trending research fields in computer vision area. Recently, deep learning-based FER models have suffered from various technological issues like under-fitting or over-fitting. It is due to either insufficient training and expression data. Motivated from the above facts, this paper presents a systematic and comprehensive survey on current state-of-art Artificial Intelligence techniques (datasets and algorithms) that provide a solution to the aforementioned issues. It also presents a taxonomy of existing facial sentiment analysis strategies in brief. Then, this paper reviews the existing novel machine and deep learning networks proposed by researchers that are specifically designed for facial expression recognition based on static images and present their merits and demerits and summarized their approach. Finally, this paper also presents the open issues and research challenges for the design of a robust facial expression recognition system.

    Original languageEnglish
    Article number9091188
    Pages (from-to)90495-90519
    Number of pages25
    JournalIEEE Access
    Publication statusPublished - 11 May 2020


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